Breast Contour Extraction and Pectoral Muscle Segmentation in Digital Mammograms
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(IJCSIS) International Journal of Computer Science and Information Security, Breast Contour Extraction and Pectoral Muscle Segmentation in Digital Mammograms Arun Kumar M.N H.S. Sheshadri Research Scholar, Department of Electronics and Department of Electronics and Communication Communication Engineering Engineering P.E.S. College of Engneering P.E.S. College of Enginering Mandya, India Mandya, India email@example.com firstname.lastname@example.org Abstract— Breast cancer is one of the major causes of fatality systems are quite high, the false positive detection rates are among women aged above 40. Digital mammography is used by also high. Accordingly, work continues on improving all radiologists for analysis and interpretation of cancer. Visual aspects of computer-aided detection (CAD) for reading and interpretation of mammograms is a very demanding mammography. Implementation of breast border detection, and expensive job. Even well-trained experts may have an interobserve variation rate of 65-75 percent. Extraction of the because of some factors such as the low contrast near the breast contour and pectoral muscle segmentation is necessary in borders, image noise and artifacts is complicated. order to limit the search for abnormalities by Computer Aided Diagnosis (CAD). A new technique for breast border extraction In mammogram, image processing [27-31] and computer- and pectoral muscle segmentation is explored in this paper. The aided diagnosis of breast cancer breast segmentation is an technique is applied to 250 MIAS mammograms. This method important pre-processing step. The accuracy and efficiency of has given about 98% in segmenting the pectoral muscle. processing algorithms will be increased if the processing is limited to a specific target region in an image. Keywords –Image Processing, mammography, morphology, filter, edge detection. Extracting the pectoral muscle [23, 24, 25] is particularly important in automated mammogram image assessment. I. INTRODUCTION Segmentation of the pectoral muscle is a non-trivial, complex and demanding task. It is also complicated further by a One of the leading causes of death among women is the number of factors. Foremost thing is, the muscle edge is not a breast cancer. Early diagnosis and subsequent treatment can straight line, but can be convex, concave or a mixture of both. significantly improve the chance of survival for patients with Secondly muscle edge though may appear to be visually breast cancer. Most effective method for the detection of early continuous; the edge exhibits variations in texture and breast cancer is mammography. Mammograms are among the sharpness. This paper describes a new technique for extracting most difficult radiological images to interpret by radiologists. the breast border and segmenting the pectoral muscle of digital Studies have shown that radiologists do not detect all breast mammograms. cancers that are retrospectively detected on the mammograms. Detection is the ability to identify potential abnormalities, The remainder of this paper is organized as follows. In such as microcalcification, masses, and architectural Section 2, the approaches to extraction of breast border and distortions. Diagnosis is the ability to characterize or classify segmentation of pectoral muscle are described. The theory and a detected abnormal entity as being either benign or malignant. proposed techniques are presented in Section 3. Experimental However, before CADe algorithms can perform their task of results are given and discussed in Section 4. Finally, the paper identifying suspicious regions in a mammogram, a series of is summarized in Section 5. pre-processing steps must be taken. These include: mammogram orientation, label and artifact removal, II. PREVIOUS APPROACHES TO BREAST BORDER mammogram enhancement, breast contour detection and EXTRACTION AND PECTORAL MUSCLE pectoral muscle segmentation SEGMENTATION Many computer algorithms [1, 2, 3] have been proposed There have been various approaches to the task of for automating various aspects of detecting the presence of isolating the breast region. cancer in mammograms. While detection rates for automatic 53 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, M. Wirth et al. developed an algorithm  that uses  are implemented on a number of mammogram images by morphological preprocessing and fuzzy rule-based algorithm Ayman et.al. The segmentation outputs of these methods were for breast region extraction. Kostas Marias et al.  used the very efficient and excellent. Method proposed in  applies boundary extraction technique based on a combination of the the meta-heuristic methods such as Ant Colony Optimization Hough transform followed by image gradient operators and (ACO) and Genetic Algorithm (GA) for identification of morphology in order to make coherent the breast region part of suspicious region in mammograms. the image. Histogram equalization and thresholding process are employed by Barba J. Leiner et al.  to extract only the There have been various approaches to the task of region of the image that corresponds to the breast. segmenting the pectoral muscle. Segmentation of the breast region in mammograms has traditionally been achieved using methods besides active A histogram-based thresholding technique is used by K. contours . Semmlow et al.  used a spatial filter and Sobel Thangavel and M. Karnan  to separate the pectoral muscle edge detector to locate the breast boundary on region. For selecting the threshold value the global optimum xeromammograms. Global thresholding has been used in is considered. The intensity values smaller than global many cases to segment the breast region from the background optimum threshold are changed to zero, and the gray values [6-7]. The major problem with using global thresholding is the greater than the threshold are changed to one. To better nonuniform background region, although efforts, such as that preserve the pectoral muscle region erosion and dilation of Masek et al.  using local thresholding have shown more operations are applied. To segment the pectoral muscle region promise. the gray level mammogram image is converted to binary image. The white pixels in the lower left corner of the A system of masking images with different thresholds to mammogram image indicate the pectoral muscle region. find the breast edge is developed by Abdel-Mottaleb et al. . Gradient based method is proposed by Méndez et al.  to Kwork et al.  developed a method for automatic find the breast contour. They used a two level thresholding pectoral muscle segmentation on mammograms by straight technique to isolate the breast region of the mammogram. The line estimation and cliff detection. A straight line estimates the smoothed mammogram is divided into three regions and then muscle edge and cliff detection refines the detected edge by a tracking algorithm is applied to the mammogram to detect surface smoothing and edge detection in a restricted the border. Bick et al.  proposed a global segmentation neighborhood. approach that incorporates aspects of thresholding, region growing and morphological filtering. Lou et al.  proposed H. Mirzaalian et al. developed  a new method for the a method based on the assumption that the trace of intensity identification of the pectoral muscle in MLO mammograms. values from the breast region to the air-background is a The developed method is based on nonlinear diffusion monotonic decreasing function. algorithm. They compared their results by those recognized by two expert radiologists. To evaluate the accuracy of proposed One of the inherent limitations of these methods is the method, HDM (Hausdorff Distance Measure) and MAEDM fact that very few of them preserve the skin or nipple. The (Mean of Absolute Error Distance Measure) were used. most promising method of extracting the breast contour focuses on modeling the non-breast region of a mammogram R.J. Ferrari proposed  a new method for the using a polynomial method, as described by Chandrasekhar identification of the pectoral muscle in MLO mammograms and Attikiouzel [13, 14]. based upon a multiresolution technique using Gabor wavelets. This new method overcomes the limitation of the straight-line Maysam Shahedi et al. proposed a new algorithm  for representation considered in their initial investigation. The automatic breast border detection in digital mammograms results of the Gabor-filter-based method indicated low based on local adaptive thresholding method. Roshan Hausdorff distances with respect to the hand-drawn pectoral Dharshana Yapa et.al. presented a new algorithm  for muscle edges. estimating skin-line and breast segmentation using fast marching algorithm. They introduced some modifications to Mario Mustra et al.  uses wavelet decomposition, the traditional fast marching method, specifically to improve image blurring and edge detection using the Sobel filter for the accuracy of skin-line estimation and breast tissue breast border detection and pectoral muscle segmentation. N. segmentation. Nicolau et al.  proposed the use of Independent Component Analysis (ICA) for identification and subsequent The method proposed in  initially determines removal of the pectoral muscle. intensity value of the background to be able to find pixels that create the border line. Then breast centre has been taken as III. PROPOSED BREAST BORDER EXTRACTION AND the starting point for a simple region growing algorithm. H. PECTORAL MUSCLE SEGMENTATION TECHNIQUE Mirzaalian et al. proposed an algorithm  based on polynomial modeling to detect breast contour. Two methods 54 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, The block diagram for pectoral muscle segmentation is shown in Fig. 1. Short description of each block is given. Mammogram input (a) (b) Breast Border Detection Figure 2: Results for MIAS image mdb003 (a). Original image; (b). Artifacts removed in the mdb003 Edge Detection and Filtering Techniques Locate the Region Containing the Pectoral Muscle This step uses the Sobel edge detector followed by dithering and 2-D order statistic filtering. The Sobel method finds edges using the Sobel approximation to the derivative. Wavelet Decomposition Edge detection is followed by dithering. A logical OR operation is done on dithered and edge detected image. A 2-D order static filtering is applied on the image obtained as a result of the previous steps. The result for mdb003 is shown in Fig. 3 after applying these steps. Mammogram with Pectoral Muscle Segmentation Figure 1: Steps carried out for pectoral muscle segmentation. 3.1 Breast Border Detection (a) (b) (c) We explored a new technique for breast region segmentation using morphological and filtering techniques. The steps followed to detect the breast border involves: - Figure 3: Results for MIAS image mdb003 (a). Edge detection; (b). Dithering Removal of noise by median filter, Artifacts removal by ; (c). 2-D statistic filtering morphological operation, Edge detection using Sobel method, filtering, finding the perimeter of the binarized image and thus Multidimensional image filtering detect the breast border. This step removes the noises using a multidimensional Removal of Noise image filtering. A rotationally symmetric Gaussian low pass filter filters the image. After that the image is converted to Median filter is used to remove the noise. It is the binary image and erosion is carried out. Fig. 4 shows the nonlinear filter used to remove the impulsive noise from an results for MIAS image mdb003 after applying these steps. image. Median filter is a spatial filtering operation. In the proposed median filter output pixel contains the median value in the 3X3 neighborhood around the corresponding pixel in the input image. Artifacts Removal The original mammogram is opened by using a suitable structuring element. After the opening of mammogram it is Figure 4: Results for MIAS image mdb003 reconstructed. Next step is to threshold the difference image with 102, which is experimentally obtained. Finally Find perimeter pixels in binary image and superimpose on the morphological operators are applied to smooth irregularities original image and expand region. Fig. 2 shows the results of these steps on MIAS image mdb003. Finally the perimeter pixels in binary image are found. This perimeter is the boundary of the breast image. Fig. 5 55 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, shows the results. A pixel is the part of the perimeter if it is Now a line FG is drawn parallel to the line BD through E. It nonzero and it is connected to at least one zero-valued pixel. can be seen that for all the 250 images the reduced rectangle The connectivity used is 8. AFGD still include the pectoral muscle. Fig. 8 shows this result for mdb016. Figure 5: Contour superimposed on original image mdb003. 3.2 Locate the region containing the pectoral muscle Pectoral muscle detection is a challenging task in the Figure 8: The reduced area that containing the pectoral muscle region is breast segmentation process. The algorithm for pectoral enclosed in AFGD. muscle segmentation proposed in this paper consists of few steps. Technique for segmenting pectoral muscle presented in this paper uses wavelet decomposition, and edge detection 3.3 Wavelet decomposition using the Canny filter. Wavelet decomposition of fourth level is being done. The region of interest containing pectoral muscle is Fourth level wavelet decomposition gives the best results for determined by two steps. First a rectangle which encloses the detecting larger structures, such as pectoral muscle. The fourth pectoral muscle is determined and then a refinement/reduction level decomposition gives the best results because it preserves to this rectangle is done so that the processing time for enough rough details while at the same time remove fine pectoral muscle segmentation can be still reduced. The initial details like noise and granulation. In this paper, a Daubechies rectangle is formed by three points A B and C. For example, if filter has been used. Daubechies wavelets are a family of the image shows MLO view of the right breast, the first point orthogonal wavelets defining a discrete wavelet transform and A is top left corner of the image with coordinates (1,1). The characterized by a maximal number of vanishing moments for second point B is determined by the contour of skin-air some given support. With each wavelet type of this class, there interface. The third point C is chosen to be approximately at is a scaling function which generates an orthogonal half of image height. By those three points a rectangle is multiresolution analysis. Fig 9 shows a Daubechies 20 2-d determined. Fig. 7 shows the breast contour superimposed on wavelet. the image mdb016 and the rectangle ABCD determined. Figure 7: Breast contour superimposed on the image mdb016 and the rectangle ABCD determined. Figure 9 : Daubechies 20 2-d wavelet The reason to reduce the size of the rectangle is to reduce After the wavelet decomposition edges that were detected the processing time for pectoral muscle segmentation and is by the Canny filter inside the pectoral muscle region are done in the following way. A new point E is determined on the removed by approximating muscle boundary with a straight breast contour in such a way that point E on the breast contour line that connects upper right corner and lower left corner of has a maximum distance from the line BD towards point A. muscle region in the case of the right breast image. 56 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Some of the results of the proposed method for pectoral muscle identification is explained below. Fig. 12 shows the IV. EXPERIMENTAL RESULTS successful results of the proposed method. The proposed method applied to 250 mammograms from Mammography Image Analysis Society (MIAS) database . The various results obtained are discussed below. Evaluation of breast contour detected in the mammograms was performed by the Hausdorff Distance Measure (HDM)  and also the Mean of Absolute Error Distance Measure (MAEDM). Evaluation is based on a distance transforms and image algebra between the edges identified by radiologists and by proposed method. The accuracy of contour detection is 99.06. (a) (b) (c) Some of the results of the proposed method for breast contour extraction are explained below. Fig. 10 shows the successful results of the proposed method. Fig. 11 shows the failure case. (d) (e) Figure 12: Pectoral muscle identification results for MIAS image mdb016. (a).Breast contour superimposed on original image; (b). The region of interest that contain the pectoral muscle; (c). Segmented area that contain the pectoral (a) (b) (c) muscle; (d). Wavelet decomposed image; (e). Pectoral muscle edge identified on image. V. CONCLUSION. In this paper a method for the detection of the breast contour and pectoral muscle segmentation is presented. The (d) proposed method for detecting the breast border contour is Figure 10: Mammogram segmentation results for MIAS image mdb016. (a). tested on the 250 MIAS datasets. This method gave 99.06 Original Mammogram; (b). Noise & Artifacts removal after filtering and successes in detecting the correct skin-air interface. The morphological operation. (c). Binary Image; (d). Contour superimposed on proposed method fails in detecting the correct skin-air original. interface for very few mammograms because of the noise (big size artifacts). Advantage of this method is low algorithm complexity and therefore short processing time. Our further development concerns smoothing of the breast border and pectoral muscle segmentation line. The proposed technique is fully autonomous, and is able to preserve the skin and nipple. Pectoral muscle detection is a challenging task because it is not very well differenced from the surrounding breast tissue. There is different intensity variation of the pectoral muscle and the surrounding tissue for each mammogram images. The (a) (b) (c) method proposed in this paper uses wavelet decomposition. This approach works well with an accuracy of 98% because Figure 11: Mammogram segmentation results for MIAS mdb012. (a). Original pectoral muscle is rather large object for detection. Future Mammogram; (b). Image after removal of artifacts; (c) Contour work will focus on smoothening the breast contour and superimposed on original image. pectoral muscle edge. 57 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, REFERENCES  Roshan Dharshana Yapa, and Koichi Harada, “Breast Skin-Line Estimation and Breast Segmentation in Mammograms using Fast-Marching  M. Wirth, D. Nikitenko, and J. 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Georgiou, M. Polycarpou, and M. Brady, “Digital Mammography: Towards Pectoral Muscle Removal via Independent Component Anlysis”, Department of Electrical and Computer Engineering, Dr. H.S. Sheshadri is working as a Professor in the University of Cyprus, 1678 Nicosia, CyprusFax. And Wolfson Medical Department of Electronics & Communication Engineering, Vision Laboratory, Oxford University, Oxford OX2 7DD, UK. PES College of Engineering Mandya, Karnataka. He received his B.E from University of Mysore in 1980 and Ph.D from AUTHORS PROFILE PSG Institute of Technology , Coimbatore, Tamilnadu, India. Arun kumar M.N is a research scholar in PES college of He has published many research papers in International Engineering, Mandya, Karnataka, India. He graduated from Journals. His research area includes Image Processing, and Mysore University in Computer Science and Engineering in Computer Vision. 1996. He received his M.Sc(Engg.) from Visvesvaraya Technological University, Belgaum, Karnataka. His research interest includes Data Mining, and Image Processing. 59 http://sites.google.com/site/ijcsis/ ISSN 1947-5500